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A novel decision-making framework based on probabilistic linguistic term set for selecting sustainable supplier considering social credit

    Yuanxiang Dong Affiliation
    ; Xumei Zheng Affiliation
    ; Zeshui Xu Affiliation
    ; Weijie Chen Affiliation
    ; Hongbo Shi Affiliation
    ; Ke Gong Affiliation

Abstract

Sustainable supplier selection (SSS) has become an essential task for decision-makers in competitive environments. We construct a new decision-making framework for SSS. First, classical SSS usually includes fixed factors in environmental, social and economic dimensions. Differently, we adopt new social factors from credit perspective with corporate social credit system being promoted vigorously by the Chinese government. Next, we employ probabilistic linguistic term sets (PLTSs) to collect experts’ judgments about interactive influence between factors. Third, we combine PLTSs with Decision Making Trial and Evaluation Laboratory (DEMATEL) method to identify critical success factors (CSFs) for improving decision-making efficiency. And we also give definition to relative importance degree, standard relative importance degree, deviation of importance degree and influence degree to reflect the interactive influence between factors. To eliminate subjective influence, we combine entropy weighting approach and DEMATEL to compute weights. Fourthly, we redefine dominance degree and apply it into TODIM method for SSS. Finally, the proposed decision-making framework’s effectiveness is verified by using the case study of a new energy vehicle (NEV) company. Based on this, sensitivity analysis and comparison of methods are conducted. The results verify that the decision-making framework is valid and effective for SSS.


First published online 14 September 2021

Keyword : sustainable supplier selection, social credit, probabilistic linguistic term sets, critical success factors, DEMATEL, TODIM

How to Cite
Dong, Y., Zheng, X., Xu, Z., Chen, W., Shi, H., & Gong, K. (2021). A novel decision-making framework based on probabilistic linguistic term set for selecting sustainable supplier considering social credit . Technological and Economic Development of Economy, 27(6), 1447-1480. https://doi.org/10.3846/tede.2021.15351
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Nov 18, 2021
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References

Ahi, P., & Searcy, C. (2013). A comparative literature analysis of definitions for green and sustainable supply chain management. Journal of Cleaner Production, 52, 329–341. https://doi.org/10.1016/j.jclepro.2013.02.018

Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied Soft Computing, 12(6), 1668–1677. https://doi.org/10.1016/j.asoc.2012.01.023

Bai, C., & Sarkis, J. (2010). Integrating sustainability into supplier selection with grey system and rough set methodologies. International Journal of Production Economics, 124, 252–264. https://doi.org/10.1016/j.ijpe.2009.11.023

Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., & Omid, M. (2018). Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers & Operations Research, 89, 337–347. https://doi.org/10.1016/j.cor.2016.02.015

Blome, C., Hollos, D., & Paulraj, A. (2014). Green procurement and green supplier development: antecedents and effects on supplier performance. International Journal of Production Research, 52(1), 32–49. https://doi.org/10.1080/00207543.2013.825748

Boudaghi, E., & Farzipoor Saen, R. (2018). Developing a novel model of data envelopment analysis– discriminant analysis for predicting group membership of suppliers in sustainable supply chain. Computers & Operations Research, 89, 348–359. https://doi.org/10.1016/j.cor.2017.01.006

Busse, C., Schleper, M. C., Niu, M., & Wagner, S. M. (2016). Supplier development for sustainability: contextual barriers in global supply chains. International Journal of Physical Distribution & Logistics Management, 46(5), 442–468. https://doi.org/10.1108/IJPDLM-12-2015-0300

Chaharsooghi, S. K., & Ashrafi, M. (2014). Sustainable supplier performance evaluation and selection with neofuzzy TOPSIS method. International Scholarly Research Notices, 2014, 434168. https://doi.org/10.1155/2014/434168

Chen, Y. J. (2011). Structured methodology for supplier selection and evaluation in a supply chain. Information Sciences, 181(9), 16511670. https://doi.org/10.1016/j.ins.2010.07.026

Chen, Y. T., Chen, C. H., Wu, S., & Lo, C. C. (2018). A two-step approach for classifying music genre on the strength of AHP weighted musical features. Mathematics, 7(1), 19. https://doi.org/10.3390/math7010019

China Environmental Protection Industry Association. (2018). Environmental protection enterprise credit evaluation index system (T/CAEPI 15-2018). https://www.uedoc.com/view/15606328.html

Chinese State Council. (2014). The development plan for the industry of energy-efficient vehicle and new energy vehicle (2011–2020). http://www.nea.gov.cn/2012-07/10/c_131705726.htm

Cuthbertson, R., Cetinkaya, B., Ewer, G., Klaas-Wissing, T., Piotrowicz, W., & Tyssen, C. (2011). Sustainable supply chain management. Springer. https://doi.org/10.1007/978-3-642-12023-7

Daultani, Y., Goswami, M., Vaidya, O. S., & Kumar, S. (2019). Inclusive risk modeling for manufacturing firms: a Bayesian network approach. Journal of Intelligent Manufacturing, 30(8), 2789–2803. https://doi.org/10.1007/s10845-017-1374-7

Elkington, J. (1998). Partnerships from cannibals with forks: The triple bottom line of 21st‐century business. Environmental Quality Management, 8(1), 37–51. https://doi.org/10.1002/tqem.3310080106

Fallahpour, A., Olugu, E. U., Musa, S. N., Wong, K. Y., & Noori, S. (2017). A decision support model for sustainable supplier selection in sustainable supply chain management. Computers & Industrial Engineering, 105, 391–410. https://doi.org/10.1016/j.cie.2017.01.005

Foroozesh, N., Tavakkoli-Moghaddam, R., & Mousavi, S. M. (2018). An interval-valued fuzzy statistical group decision making approach with new evaluating indices for sustainable supplier selection problem. Journal of Intelligent & Fuzzy Systems, 36(2), 1855–1866. https://doi.org/10.3233/JIFS-17467

Gao, J., Xu, Z. S., Ren, P. J., & Liao, H. C. (2019). An emergency decision making method based on the multiplicative consistency of probabilistic linguistic preference relations. International Journal of Machine Learning and Cybernetics, 10(7), 1613–1629. https://doi.org/10.1007/s13042-018-0839-0

Garg, C., Sharma, A., & Goyal, G. (2017). A hybrid decision model to evaluate critical factors for successful adoption of GSCM practices under fuzzy environment. Uncertain Supply Chain Management, 5(1), 59–70. https://doi.org/10.5267/j.uscm.2016.7.002

General Administration of Customs of the People’s Republic of China. (2019). Customs statistics. http://www.customs.gov.cn/customs/302249/302274/302277/3250476/index.html

Girubha, J., Vinodh, S., & Vimal, K. E. K. (2016). Application of interpretative structural modelling integrated multi criteria decision making methods for sustainable supplier selection. Journal of Modelling in Management, 11(2), 358–388. https://doi.org/10.1108/JM2-02-2014-0012

Gören, H. G. (2018). A decision framework for sustainable supplier selection and order allocation with lost sales. Journal of Cleaner Production, 183, 1156–1169. https://doi.org/10.1016/j.jclepro.2018.02.211

Gou, X. J., & Xu, Z. S. (2016). Novel basic operational laws for linguistic terms, hesitant fuzzy linguistic term sets and probabilistic linguistic term sets. Information Sciences, 372, 407–427. https://doi.org/10.1016/j.ins.2016.08.034

Govindan, K., Khodaverdi, R., & Jafarian, A. (2013). A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production, 47, 345–354. https://doi.org/10.1016/j.jclepro.2012.04.014

Graham, G., Freeman, J., & Chen, T. (2015). Green supplier selection using an AHP-Entropy-TOPSIS framework. Supply Chain Management: An International Journal, 20(3), 327–340. https://doi.org/10.1108/SCM-04-2014-0142

Haeri, S. A. S., & Rezaei, J. (2019). A grey-based green supplier selection model for uncertain environments. Journal of Cleaner Production, 221, 768–784. https://doi.org/10.1016/j.jclepro.2019.02.193

Hendiani, S., Liao, H., Ren, R., & Lev, B. (2020). A likelihood-based multi-criteria sustainable supplier selection approach with complex preference information. Information Sciences, 536, 135–155. https://doi.org/10.1016/j.ins.2020.05.065

Hoseini, A. R., Ghannadpour, S. F., & Ghamari, R. (2020). Sustainable supplier selection by a new possibilistic hierarchical model in the context of Z-information. Journal of Ambient Intelligence and Humanized Computing, 11, 4827–4853. https://doi.org/10.1007/s12652-020-01751-3

Hou, J., Zhang, Q., Hu, S., & Chen, D. (2020). Evaluation of a new extended producer responsibility mode for WEEE based on a supply chain scheme. Science of The Total Environment, 726, 138531. https://doi.org/10.1016/j.scitotenv.2020.138531

Hu, K. H., Chen, F. H., Hsu, M. F., & Tzeng, G. H. (2021). Identifying key factors for adopting artificial intelligence-enabled auditing techniques by joint utilization of fuzzy-rough set theory and MRDM technique. Technological and Economic Development of Economy, 27(2), 459–492. https://doi.org/10.3846/tede.2020.13181

Igarashi, M., de Boer, L., & Fet, A. M. (2013). What is required for greener supplier selection? A literature review and conceptual model development. Journal of Purchasing and Supply Management, 19(4), 247–263. https://doi.org/10.1016/j.pursup.2013.06.001

Jabbar, F. K., Grote, K., & Tucker, R. E. (2019). A novel approach for assessing watershed susceptibility using weighted overlay and analytical hierarchy process (AHP) methodology: a case study in Eagle Creek Watershed, USA. Environmental Science and Pollution Research, 26, 31981–31997. https://doi.org/10.1007/s11356-019-06355-9

Jia, P., Govindan, K., Choi, T. M., & Rajendran, S. (2015). Supplier selection problems in fashion business operations with sustainability considerations. Sustainability, 7(2), 1603–1619. https://doi.org/10.3390/su7021603

Kannan, D., Mina, H., Nosrati-Abarghooee, S., & Khosrojerdi, G. (2020). Sustainable circular supplier selection: A novel hybrid approach. The Science of the Total Environment, 722, 137936–137936. https://doi.org/10.1016/j.scitotenv.2020.137936

Kaur, P. (2014). Selection of vendor based on intuitionistic fuzzy analytical hierarchy process. Advances in Operations Research, 2014, 987690. https://doi.org/10.1155/2014/987690

Khan, M. A., Mittal, S., West, S., & Wuest, T. (2018). Review on upgradability – A product lifetime extension strategy in the context of product service systems. Journal of Cleaner Production, 204, 1154–1168. https://doi.org/10.1016/j.jclepro.2018.08.329

Khoshfetrat, S., Rahiminezhad Galankashi, M., & Almasi, M. (2020). Sustainable supplier selection and order allocation: a fuzzy approach. Engineering Optimization, 52(9), 1494–1507. https://doi.org/10.1080/0305215X.2019.1663185

Lei, F., Lu, J., Wei, G., Wu, J., Wei, C., & Guo, Y. (2020a). GRA method for waste incineration plants location problem with probabilistic linguistic multiple attribute group decision making. Journal of Intelligent & Fuzzy Systems, 39(3), 2909–2920. https://doi.org/10.3233/JIFS-191443

Lei, F., Wei, G., Wu, J., Wei, C., & Guo, Y. (2020b). QUALIFLEX method for MAGDM with probabilistic uncertain linguistic information and its application to green supplier selection. Journal of Intelligent & Fuzzy Systems, 39(5), 6819–6831. https://doi.org/10.3233/JIFS-191737

Liao, H., Ren, R., Antucheviciene, J., Šaparauskas, J., & Al-Barakati, A. (2020). Sustainable construction supplier selection by a multiple criteria decision-making method with hesitant linguistic information. E&M Economics and Management, 23(4), 119–136. https://doi.org/10.15240/tul/001/2020-4-008

Lin, M., Wang, H., Xu, Z. S., Yao, Z., & Huang, J. (2018a). Clustering algorithms based on correlation coefficients for probabilistic linguistic term sets. International Journal of Intelligent Systems, 33(12), 2402–2424. https://doi.org/10.1002/int.22040

Lin, S., Li, C., Xu, F., Liu, D., & Liu, J. (2018b). Risk identification and analysis for new energy power system in China based on D numbers and decision-making trial and evaluation laboratory (DEMATEL). Journal of Cleaner Production, 180, 81–96. https://doi.org/10.1016/j.jclepro.2018.01.153

Liu, A., Xiao, Y., Lu, H., Tsai, S. B., & Song, W. (2019a). A fuzzy three-stage multi-attribute decisionmaking approach based on customer needs for sustainable supplier selection. Journal of Cleaner Production, 239, 118043. https://doi.org/10.1016/j.jclepro.2019.118043

Liu, H. C., Quan, M. Y., Li, Z., & Wang, Z. L. (2019b). A new integrated MCDM model for sustainable supplier selection under interval-valued intuitionistic uncertain linguistic environment. Information Sciences, 486, 254–270. https://doi.org/10.1016/j.ins.2019.02.056

Liu, Z., Hao, H., Cheng, X., & Zhao, F. (2018). Critical issues of energy efficient and new energy vehicles development in China. Energy Policy, 115, 92–97. https://doi.org/10.1016/j.enpol.2018.01.006

Liu, Z., Ming, X., & Song, W. (2019c). A framework integrating interval-valued hesitant fuzzy DEMATEL method to capture and evaluate co-creative value propositions for smart PSS. Journal of Cleaner Production, 215, 611–625. https://doi.org/10.1016/j.jclepro.2019.01.089

Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686–1698. https://doi.org/10.1016/j.jclepro.2016.09.078

Memari, A., Dargi, A., Jokar, M. R. A., Ahmad, R., & Rahim, A. R. A. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9–24. https://doi.org/10.1016/j.jmsy.2018.11.002

Mi, X., Liao, H. C., Liao, Y., Lin, Q., & Al-Barakati, A. (2020). Green suppler selection by an integrated method with stochastic acceptability analysis and multimoora. Technological and Economic Development of Economy, 26(3), 549–572. https://doi.org/10.3846/tede.2020.11964

Ministry of Commerce of the People’s Republic of China. (2019). The evaluation criteria for corporatesocial-responsibility of business service industry (SB/T 10963-2013). http://www.gbstandards.org/GB_standard_english.asp?code=SB/T%2010963-2013&word=The%20evaluation%20criteria%20for%20co

National Standardization Administration of China. (2015a). Index of enterprise credit evaluation (GB/T 23794-2015). https://www.chinesestandard.net/PDF/English.aspx/GBT23794-2015

National Standardization Administration of China. (2015b). Enterprise integrity management system (GB/T 31950-2015). http://whly.gd.gov.cn/gd_zww/upload/file/file/201706/19093213dz6i.pdf

Negash, Y. T., Kartika, J., Tseng, M. L., & Tan, K. (2020). A novel approach to measure product quality in sustainable supplier selection. Journal of Cleaner Production, 252, 119838. https://doi.org/10.1016/j.jclepro.2019.119838

Neumüller, C., Lasch, R., & Kellner, F. (2016). Integrating sustainability into strategic supplier portfolio selection. Management Decision, 54(1), 194–221. https://doi.org/10.1108/MD-05-2015-0191

Pang, Q., Wang, H., & Xu, Z. S. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Sciences, 369, 128–143. https://doi.org/10.1016/j.ins.2016.06.021

Parajuli, R., Thoma, G., & Matlock, M. D. (2019). Environmental sustainability of fruit and vegetable production supply chains in the face of climate change: A review. Science of the Total Environment, 650, 2863–2879. https://doi.org/10.1016/j.scitotenv.2018.10.019

Peng, J. J., Tian, C., Zhang, W. Y., Zhang, S., & Wang, J. Q. (2020). An integrated multi-criteria decisionmaking framework for sustainable supplier selection under picture fuzzy environment. Technological and Economic Development of Economy, 26(3), 1–26. https://doi.org/10.3846/tede.2020.12110

Phochanikorn, P., & Tan, C. (2019). A new extension to a multi-criteria decision-making model for sustainable supplier selection under an intuitionistic fuzzy environment. Sustainability, 11(19), 5413. https://doi.org/10.3390/su11195413

Rashidi, K., & Cullinane, K. (2019). A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy. Expert Systems with Applications, 121, 266–281. https://doi.org/10.1016/j.eswa.2018.12.025

Rodriguez, R. M., Martinez, L., & Herrera, F. (2011). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109–119. https://doi.org/10.1109/TFUZZ.2011.2170076

Sahu, A. K., Datta, S., & Mahapatra, S. S. (2016). Evaluation and selection of resilient suppliers in fuzzy environment. Benchmarking: An International Journal, 23(3), 651–673. https://doi.org/10.1108/BIJ-11-2014-0109

Senvar, O., Tuzkaya, G., & Kahraman, C. (2014). Multi criteria supplier selection using fuzzy PROMETHEE method. In Supply chain management under fuzziness (pp. 21–34). Springer. https://doi.org/10.1007/978-3-642-53939-8_2

Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699–1710. https://doi.org/10.1016/j.jclepro.2008.04.020

Song, W., Xu, Z. T., & Liu, H. C. (2017). Developing sustainable supplier selection criteria for solar air-conditioner manufacturer: An integrated approach. Renewable and Sustainable Energy Reviews, 79, 1461–1471. https://doi.org/10.1016/j.rser.2017.05.081

Song, Y., & Li, G. (2019). A large-scale group decision-making with incomplete multi-granular probabilistic linguistic term sets and its application in sustainable supplier selection. Journal of the Operational Research Society, 70(5), 827–841. https://doi.org/10.1080/01605682.2018.1458017

Szegedi, K., & Kerekes, K. N. (2012). Challenges of responsible supply chain management. Club of Economics in Miskolc, 8(2), 68–75.

The State Council. (2012). The energy-saving and new energy vehicle industry development plan (2012– 2020) (No. Guofa [2012] 22). Beijing.

The State Council. (2014, June 27). Outline of the plan for the construction of the social credit system (2014–2020). http://www.gov.cn/xinwen/2014-06/27/content_2708964.htm

The State Council. (2017, December 1). “Five years of endeavor” in the construction of social credit system. https://www.creditchina.gov.cn/xinyongyanjiu/xinyongyanjiuhuicui/201712/t20171201_98144.html

The State Council. (2021, March 4). Green credit system. https://www.creditchina.gov.cn/xinyongyanjiu/xinyongshalong/202103/t20210304_228711.html

Tzeng, G. H., Chiang, C. H., & Li, C. W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert systems with Applications, 32(4), 1028–1044. https://doi.org/10.1016/j.eswa.2006.02.004

Underwriters Laboratories. (2011). Sustainability for manufacturing organizations (UL 880-2011). http://std600.infoeach.com/view-NjAwfDIwMDQ4NQ==.html

Wang, S., Wei, G., Wu, J., Wei, C., & Guo, Y. (2021). Model for selection of hospital constructions with probabilistic linguistic GRP method. Journal of Intelligent & Fuzzy Systems, 40(1), 1245–1259. https://doi.org/10.3233/JIFS-201543

Wang, Y., Li, L., Chen, C., Huang, C., Huang, H., Feng, J., Wang, S., Wang, H., Zhang, G., Zhou, M., Cheng, P., Wu, M., Sheng, G., Fu, J., Hu, Y., Russell, A. G., & Wumaer, A. (2014). Source apportionment of fine particulate matter during autumn haze episodes in Shanghai, China. Journal of Geophysical Research: Atmospheres, 119(4), 1903–1914. https://doi.org/10.1002/2013JD019630

Wei, C., Wu, J., & Guo, Y., & Wei, G. (2021a). Green supplier selection based on CODAS method in probabilistic uncertain linguistic environment. Technological and Economic Development of Economy, 27(3), 530–549. https://doi.org/10.3846/tede.2021.14078

Wei, G., Wei, C., Wu, J., & Guo, Y. (2021b). Probabilistic linguistic multiple attribute group decision making for location planning of electric vehicle charging stations based on the generalized Dice similarity measures. Artificial Intelligence Review, 54, 4137–4167. https://doi.org/10.1007/s10462-020-09950-2

Wei, G., Wei, C., Wu, J., & Wang, H. (2019). Supplier selection of medical consumption products with a probabilistic linguistic MABAC method. International Journal of Environmental Research and Public Health, 16(24), 5082. https://doi.org/10.3390/ijerph16245082

Wilson, E. J. (1994). The relative importance of supplier selection criteria: a review and update. International Journal of Purchasing and Materials Management, 30(2), 34–41. https://doi.org/10.1111/j.1745-493X.1994.tb00195.x

Wisner, J. D., Tan, K.-C., & Leong, G. K. (2014). Principles of supply chain management: A balanced approach. Cengage Learning.

Wittstruck, D., & Teuteberg, F. (2012). Integrating the concept of sustainability into the partner selection process: a fuzzy-AHP-TOPSIS approach. International Journal of Logistics Systems and Management, 12(2), 195–226. https://doi.org/10.1504/IJLSM.2012.047221

Wu, H.-H., & Chang, S.-Y. (2015). A case study of using DEMATEL method to identify critical factors in green supply chain management. Applied Mathematics and Computation, 256, 394–403. https://doi.org/10.1016/j.amc.2015.01.041

Wu, Y., Ke, Y., Xu, C., & Li, L. (2019). An integrated decision-making model for sustainable photovoltaic module supplier selection based on combined weight and cumulative prospect theory. Energy, 181, 1235–1251. https://doi.org/10.1016/j.energy.2019.06.027

Xu, Z. S. (2007). Multiple-attribute group decision making with different formats of preference information on attributes. IEEE Transactions on Systems, Man, & Cybernetics, Part B, 37(6), 1500–1511. https://doi.org/10.1109/TSMCB.2007.904832

Xu, Z. S. (2012). Linguistic decision making. Springer. https://doi.org/10.1007/978-3-642-29440-2

Xu, Z. S., & Wang, H. (2017). On the syntax and semantics of virtual linguistic terms for information fusion in decision making. Information Fusion, 34, 43–48. https://doi.org/10.1016/j.inffus.2016.06.002

Yu, C., Shao, Y., Wang, K., & Zhang, L. (2019). A Group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environment. Expert Systems with Applications, 121, 1–17. https://doi.org/10.1016/j.eswa.2018.12.010

Yu, W., Zhang, Z., Zhong, Q., & Sun, L. (2017). Extended TODIM for multi-criteria group decision making based on unbalanced hesitant fuzzy linguistic term sets. Computers & Industrial Engineering, 114, 316–328. https://doi.org/10.1016/j.cie.2017.10.029

Zhang, X., & Bai, X. (2017). Incentive policies from 2006 to 2016 and new energy vehicle adoption in 2010–2020 in China. Renewable and Sustainable Energy Reviews, 70, 24–43. https://doi.org/10.1016/j.rser.2016.11.211

Zhang, Y. X., Xu, Z. S., & Liao, H. C. (2017). A consensus process for group decision making with probabilistic linguistic preference relations. Information Sciences, 414, 260–275. https://doi.org/10.1016/j.ins.2017.06.006

Zhang, Y. X., Xu, Z. S., & Liao, H. C. (2019). Water security evaluation based on the TODIM method with probabilistic linguistic term sets. Soft Computing, 23(15), 6215–6230. https://doi.org/10.1007/s00500-018-3276-9

Zhou, X. Y., & Xu, Z. D. (2018). An integrated sustainable supplier selection approach based on hybrid information aggregation. Sustainability, 10(7), 2543. https://doi.org/10.3390/su10072543

Zimmer, K., Fröhling, M., & Schultmann, F. (2016). Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 54(5), 1412–1442. https://doi.org/10.1080/00207543.2015.1079340